The present invention relates to patient-specific simulations for planning cardiac resynchronization therapy, and more particularly, to simulations using multi-scale computational models of heart ventricles personalized from preoperative clinical data and medical images to predict outcomes of cardiac resynchronization therapy for a patient.
Patients with heart failure often present dysynchronous ventricular contraction. For example, the left ventricle (LV) and right ventricle (RV) do not beat synchronously, which decreases the efficiency of the cardiac pump. Cardiac Resynchronization Therapy (CRT) treats this condition by artificially pacing the cardiac muscle through an advanced pacemaker with several pacing leads. In order to implement CRT in a patient, a pulse generator (pacemaker) and multiple leads, including a left ventricle lead, a right ventricle lead, and a right atrial lead, are used to synchronize ventricle contraction in a patient. Although CRT is typically an efficient treatment of heart failure, thirty percent of patients do not respond to the therapy even though they are within the recommended guidelines for CRT. In such cases, the patient's heart does not improve as a result of the CRT and the ejection fraction, which is a measure of cardiac efficiency, stays constant despite the therapy.